



'''simple docstring'''



import argparse
from collections import defaultdict

import yaml


lowerCAmelCase_  :							Tuple             =    'docs/source/en/_toctree.yml'
def        UpperCAmelCase (    A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   Dict         =      defaultdict(A					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							SCREAMING_SNAKE_CASE       :   Tuple         =      [key for key, value in counts.items() if value > 1]

							SCREAMING_SNAKE_CASE       :   Tuple         =      []
							for duplicate_key in duplicates:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}					)
														if len(A					) > 1:
																					raise ValueError(
																					    F"""{duplicate_key} is present several times in the documentation table of content at """
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''					)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]					)

							# Sort
							return sorted(A       ,		key=lambda A					: s["title"].lower()					)
def        UpperCAmelCase (    A	:					Any=False					):
							with open(A       ,		encoding='''utf-8'''					) as f:
														SCREAMING_SNAKE_CASE       :   str         =      yaml.safe_load(f.read()					)

							# Get to the API doc
							SCREAMING_SNAKE_CASE       :   Any         =      0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							SCREAMING_SNAKE_CASE       :   Dict         =      content[api_idx]['''sections''']

							# Then to the model doc
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							SCREAMING_SNAKE_CASE       :   Dict         =      api_doc[model_idx]['''sections''']

							SCREAMING_SNAKE_CASE       :   List[Any]         =      [(idx, section) for idx, section in enumerate(A					) if '''sections''' in section]
							SCREAMING_SNAKE_CASE       :   int         =      False
							for idx, modality_doc in modalities_docs:
														SCREAMING_SNAKE_CASE       :   List[str]         =      modality_doc['''sections''']
														SCREAMING_SNAKE_CASE       :   List[str]         =      clean_model_doc_toc(A					)

														if old_modality_doc != new_modality_doc:
																					SCREAMING_SNAKE_CASE       :   Dict         =      True
																					if overwrite:
																												SCREAMING_SNAKE_CASE       :   int         =      new_modality_doc

							if diff:
														if overwrite:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      model_doc
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      api_doc
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
																												f.write(yaml.dump(A       ,		allow_unicode=A					)					)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Optional[int]             =    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							lowerCAmelCase_  :							Any             =    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)









'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




